这段代码两次重采样分别是对什么 for ii=1:N for jj=1:N Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B*(sqrt(u_Q)*randn+gamrnd(1,1))+sqrt(Q)randn+gamrnd(1,1); % Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B(sqrt(u_Q)*randn)+sqrt(Q)*randn;%采样获得N个粒子 ypart =detection_equation(Xsetpre(ii,jj),k); %预测值 vhat = y0 - ypart; weight(ii,jj)=1/(det(R)^(1/2))exp(-1/2vhat'*inv(R)*vhat)+ 1e-99; end %归一化 wsumii = sum(weight(ii,:)); weight_ii=weight(ii,:)./wsumii; Xset_ii=Xsetpre(ii,:); weight_pre=weight; particle_pre=Xsetpre; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_ii); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_ii); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_ii); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_ii); %多项式重采样 end %U(jj) %x(ii) weight_ii=weight_ii(outIndex); part_ii=Xset_ii(outIndex); particle(ii,:)=part_ii; weight(ii,:)=weight_ii; X_ii(ii)=mean(part_ii); end wsumjj = sum(sum(weight),2); weight_u=weight./wsumjj; weight_jj=(sum(weight_u,2))'; Xset_jj=X_ii; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_jj); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_jj); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_jj); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_jj); %多项式重采样 end weight_jj=weight_jj(outIndex); part_jj=Xset_jj(outIndex); Xo=mean(part_jj)
时间: 2024-03-29 12:35:02 浏览: 80
这段代码进行了两次重采样,第一次重采样是对粒子的行进行重采样,第二次重采样是对粒子的列进行重采样。在第一次重采样后,粒子的行被重新赋予权重并进行重采样,得到新的一组粒子行。在第二次重采样中,利用第一次重采样后得到的粒子行,对粒子的列进行重采样,得到最终的估计状态量。重采样的目的是为了防止粒子的权重过于集中,导致粒子退化现象,从而提高滤波精度。
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这段代码为什么进行二级重采样 for ii=1:N for jj=1:N Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B*(sqrt(u_Q)*randn+gamrnd(1,1))+sqrt(Q)*randn+gamrnd(1,1); % Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B*(sqrt(u_Q)*randn)+sqrt(Q)*randn;%采样获得N个粒子 ypart =detection_equation(Xsetpre(ii,jj),k); %预测值 vhat = y0 - ypart; weight(ii,jj)=1/(det(R)^(1/2))*exp(-1/2*vhat'*inv(R)*vhat)+ 1e-99; end %归一化 wsumii = sum(weight(ii,:)); weight_ii=weight(ii,:)./wsumii; Xset_ii=Xsetpre(ii,:); weight_pre=weight; particle_pre=Xsetpre; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_ii); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_ii); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_ii); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_ii); %多项式重采样 end %U(jj) %x(ii) weight_ii=weight_ii(outIndex); part_ii=Xset_ii(outIndex); particle(ii,:)=part_ii; weight(ii,:)=weight_ii; X_ii(ii)=mean(part_ii); end wsumjj = sum(sum(weight),2); weight_u=weight./wsumjj; weight_jj=(sum(weight_u,2))'; Xset_jj=X_ii; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_jj); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_jj); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_jj); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_jj); %多项式重采样 end weight_jj=weight_jj(outIndex); part_jj=Xset_jj(outIndex); Xo=mean(part_jj)
这段代码是一个粒子滤波算法。粒子滤波是一种基于蒙特卡罗方法的滤波算法,用于估计状态空间模型中的状态量。其中,particle(ii,jj)表示第 ii 行、第 jj 列的粒子,Xsetpre(ii,jj)表示该粒子在当前时刻的状态,weight(ii,jj)表示该粒子的权重。process_equation(particle(ii,jj),k)表示该粒子在状态转移方程下的预测值,detection_equation(Xsetpre(ii,jj),k)表示该粒子在观测方程下的预测值。重采样过程是为了保证粒子的多样性,避免出现粒子退化现象。最后,Xo表示估计的状态量。
function [Xo,particle]=particle_filter_u2(particle,y0,B,R,Q,N,k,u_Q,ResampleStrategy) for ii=1:N for jj=1:N Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B*(sqrt(u_Q)*randn+gamrnd(1,1))+sqrt(Q)*randn+gamrnd(1,1); % Xsetpre(ii,jj) = process_equation(particle(ii,jj),k)+B*(sqrt(u_Q)*randn)+sqrt(Q)*randn;%采样获得N个粒子 ypart =detection_equation(Xsetpre(ii,jj),k); %预测值 vhat = y0 - ypart; weight(ii,jj)=1/(det(R)^(1/2))*exp(-1/2*vhat'*inv(R)*vhat)+ 1e-99; end %归一化 wsumii = sum(weight(ii,:)); weight_ii=weight(ii,:)./wsumii; Xset_ii=Xsetpre(ii,:); weight_pre=weight; particle_pre=Xsetpre; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_ii); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_ii); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_ii); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_ii); %多项式重采样 end %U(jj) %x(ii) weight_ii=weight_ii(outIndex); part_ii=Xset_ii(outIndex); particle(ii,:)=part_ii; weight(ii,:)=weight_ii; X_ii(ii)=mean(part_ii); end wsumjj = sum(sum(weight),2); weight_u=weight./wsumjj; weight_jj=(sum(weight_u,2))'; Xset_jj=X_ii; % 重采样 if ResampleStrategy==1 outIndex = randomR(weight_jj); %随机重采样 elseif ResampleStrategy==2 outIndex = residualR(weight_jj); %残差重采样 elseif ResampleStrategy==3 outIndex = systematicR(weight_jj); %系统重采样 elseif ResampleStrategy==4 outIndex = multinomialR(weight_jj); %多项式重采样 end weight_jj=weight_jj(outIndex); part_jj=Xset_jj(outIndex); Xo=mean(part_jj);
可以看出,这是一个粒子滤波算法的实现,用于估计一个系统的状态。其中,输入参数包括粒子数N、观测值y0、过程噪声Q、观测噪声R等。具体实现中,首先利用process_equation函数和随机噪声生成N个粒子;然后根据观测值和粒子状态,计算每个粒子的权重;接着进行归一化和重采样,得到新的一组粒子;最后,再次计算粒子的权重,进行归一化和重采样,得到最终的状态估计值Xo。其中,ResampleStrategy参数指定重采样策略(随机、残差、系统或多项式)。
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